Machine vision systems use image acquisition devices that include camera sensors to deliver information on a viewed subject. The system then interprets this information according to a variety of algorithms to perform a programmed decision-making and/or identification function. For an image to be most-effectively acquired by a sensor in the visible, and near-visible light range, the subject is typically illuminated.
In the example of symbology reading (also commonly termed “barcode” scanning) using an image sensor, proper illumination is highly desirable. Symbology reading entails the aiming of an image acquisition sensor (CMOS camera, CCD, etc.) at a location on an object that contains a symbol (a “barcode”), and acquiring an image of that symbol. The symbol contains a set of predetermined patterns that represent an ordered group of characters or shapes from which an attached data processor (for example a microcomputer) can derive useful information about the object (e.g. its serial number, type, model, price, etc.). Symbols/barcodes are available in a variety of shapes and sizes. Two of the most commonly employed symbol types used in marking and identifying objects are the so-called one-dimensional barcode, consisting of a line of vertical stripes of varying width and spacing, and the so-called two-dimensional barcode consisting of a two-dimensional array of dots or rectangles.
By way of background, FIG. 1 shows an exemplary scanning system 100 adapted for handheld operation. An exemplary handheld scanning appliance or handpiece 102 is depicted. It includes a grip section 104 and a body section 106. An illuminator 120, in the form of a light pipe in this example, is provided to direct light of an appropriate wavelength and angle onto an object 130 containing an illustrative two-dimensional barcode 132 on its surface. An image formation system 151, shown in phantom, can be controlled and can direct image data to an onboard embedded processor 109. This processor can include a scanning software application 113 by which lighting is controlled, images are acquired and image data is interpreted into usable information (for example, alphanumeric strings) derived from the arrangement of light and dark elements within the exemplary barcode symbol 132.
The decoded information can be directed via a cable 111 to a PC or other data storage device 112 having (for example) a display 114, keyboard 116 and mouse 118, where this information can be stored and further manipulated using an appropriate PC-based application. Alternatively, the cable 111 can be directly connected to an interface in the scanning appliance and an appropriate interface in the computer 112. In this case the remote computer-based application (not shown) performs various image interpretation/decoding and lighting control functions as needed. The particular arrangement of the handheld scanning appliance with respect to an embedded processor, computer or other processor is highly variable. For example, a wireless interconnect can be provided by which no interconnecting cable 111 is required. Likewise, the depicted microcomputer can be substituted with another processing device, including an onboard processor or a miniaturized processing unit such as a personal digital assistant or other small-scale computing device.
The scanning application 113 can be adapted to respond to inputs from the scanning appliance 102. For example, when the operator toggles a trigger 122 on the hand held scanning appliance 102, an internal camera image sensor (within the image formation system 151) acquires an image of a region of interest surrounding the subject barcode 132. The exemplary region of interest includes the two-dimensional symbol 132 that can be used to identify the object 130, or some other characteristic. Identification and other processing functions are carried out by the scanning application 113, based upon image data transmitted from the hand held scanning appliance 102 to the processor 109. A visual indicator (not shown) or other indicia can be illuminated by signals from the processor 109 to indicate successful reading and decoding of the symbol 132.
In reading symbology or other subjects of interest, the geometry of the particular symbol on the underlying surface is of particular concern. Where the symbol and/or other viewed subject is printed on a flat surface with contrasting ink or paint, the reading of the entire symbology pattern may be relatively straightforward, using a given sensor and lens setting, and appropriate levels and angles of illumination.
Conversely, where a symbol or other subject is formed on a more-irregular surface, and/or is created by etching or peening a pattern directly on the surface, the acquired image of the symbol may exhibit significant irregularity across its surface. In the present example, as detailed in FIG. 1, the object 130 defines a cylindrical surface that causes an acquired image 160 of the symbol 132 to exhibit a faded or unreadable appearance at its edges. This results from the differential reflection and/or scattering of light due to the continually varying angle of the underlying cylindrical surface. As such the image is largely readable in its middle, while being less-readable, or unreadable, at its edges. Clearly other geometries and/or surface finishes can yield differing reading problems with respect to the acquired image.
Another example of an instance in which surface, lighting or other variations can result in a partially unreadable symbol is shown in FIG. 2. In this example, the system 200 derives images from exemplary objects 210, 212, 214 provided in a moving line that can include a conveyor 220. This arrangement is similar to that described in commonly assigned, copending U.S. Published Patent Application No. US20050275831A1, entitled METHOD AND APPARATUS FOR VISUAL DETECTION AND INSPECTION OF OBJECTS, by William M. Silver, the teachings of which are expressly incorporated by reference. In this arrangement, relative movement between a machine vision detector 230 (or other imaging device) and the objects 210, 212, 214 occurs, with each object and a feature of interest (symbols 240, 242 and 244) being imaged in turn. A machine vision application, embedded in the processing circuitry of the detector 230, or in a remote computer 250, derives an image of the subject feature/symbol. As shown, the derived image 252 is partially unreadable due to the surface shape of the object, and hence, unreadable. While the exemplary machine vision detector may acquire multiple images of the object/feature of interest as it passes through the field of view, each image is used individually to perform a detection and/or triggering function.
In general, only a single image of the symbol is acquired to derive the information from the symbol in each of the above-described implementations. Any faded, or otherwise unreadable, regions in this image will result in a bad read of the symbol. This problem may be addressed, in part, by applying different types of illumination to the subject, from which the most-readable image is derived. By way of further background, commonly assigned U.S. patent application Ser. No. 11/014,478, entitled HAND HELD SYMBOLOGY READER ILLUMINATION DIFFUSER and U.S. patent application Ser. No. 11/019,763, entitled LOW PROFILE ILLUMINATION FOR DIRECT PART MARK READERS, both by Laurens W. Nunnink, the teachings of which are expressly incorporated herein by reference, provide techniques for improving the transmission of bright field (high angle) and dark field (low angle) illumination so as to provide the best type of illumination for viewing a particular subject. These techniques include the provision of particular geometric arrangements of direct, bright field LEDs and conical and/or flat diffusers that are placed between bright field illuminators and the subject to better spread the bright field light. The above-incorporated HAND HELD SYMBOLOGY READER ILLUMINATION DIFFUSER further teaches the use of particular colors for improving the illumination applicable to certain types of surfaces. Still, these techniques rely on the choice of the best (potentially most-readable) image from a plurality of acquired images, each taken under differing conditions.
In general, the typical practice of acquiring a single image (or multiple images in which only the “best” single image is chosen) often produces an acquired image in which at least part of the image is difficult, or impossible, to read. Accordingly, it is desirable to provide a system and method for enabling the reading of symbols and other predetermined forms of images that better addresses the existence of faded/unreadable regions, and other irregularities due to varying surface, lighting and other conditions on the underlying surface.